The spread of antibiotic resistant bacteria (ARB) in the environment poses a potential threat to human health, and the reactivation of inactivated ARB accelerated the spread of ARB. However, little... Show moreThe spread of antibiotic resistant bacteria (ARB) in the environment poses a potential threat to human health, and the reactivation of inactivated ARB accelerated the spread of ARB. However, little is known about the reactivation of sunlight-inactivated ARB in natural waters. In this study, the reactivation of sunlight-inactivated ARB in dark conditions was investigated with tetracycline-resistant E. coli (Tc-AR E. coli) as a representative. Results showed that sunlight-inactivated Tc-AR E. coli underwent dark repair to regain tetracycline resistance with dark repair ratios increasing from (0.124 ± 0.012)‱ within 24 h dark treatment to (0.891 ± 0.033)‱ within 48 h. The presence of Suwannee River fulvic acid (SRFA) promoted the reactivation of sunlight-inactivated Tc-AR E. coli and tetracycline inhibited their reactivation. The reactivation of sunlight-inactivated Tc-AR E. coli is mainly attributed to the repair of the tetracycline-specific efflux pump in the cell membrane. Tc-AR E. coli in a viable but non-culturable (VBNC) state was observed and dominated the reactivation as the inactivated ARB remain present in the dark for more than 20 h. These results explained the reason for distribution difference of Tc-ARB at different depths in natural waters, which are of great significance for understanding the environmental behavior of ARB. Show less
Cluster inference based on spatial extent thresholding is a popular analysis method multiple testing in spatial data, and is frequently used for finding activated brain areas in neuroimaging.... Show moreCluster inference based on spatial extent thresholding is a popular analysis method multiple testing in spatial data, and is frequently used for finding activated brain areas in neuroimaging. However, the method has several well-known issues. While powerful for finding regions with some activation, the method as currently defined does not allow any further quantification or localisation of signal. In this paper, we repair this gap. We show that cluster-extent inference can be used (1) to infer the presence of signal in any region of interest and (2) to quantify the percentage of activation in such regions. These additional inferences come for free, i.e. they do not require any further adjustment of the alpha-level of tests, while retaining full family-wise error control. We achieve this extension of the possibilities of cluster inference by embedding the method into a closed testing procedure, and solving the graph-theoretic k-separator problem that results from this embedding. We demonstrate the usefulness of the improved method in a large-scale application to neuroimaging data from the Neurovault database. Show less
Chen, X.; Poortvliet, M.T.L.; Molen, S.J. van der; Dood, M.J.A. de 2023
With the emergence of online social networks (OSNs), the way people create and share information has changed, which becomes faster and broader than traditional social media. Understanding how... Show moreWith the emergence of online social networks (OSNs), the way people create and share information has changed, which becomes faster and broader than traditional social media. Understanding how information (both good and harmful) spreads through OSNs, as well as what elements drive the success of information diffusion, has significant implications for a wide range of real-world applications. In this thesis, we conduct research to analysis the diffusion of information in OSNs via using deep representation learning. Specifically, we aim to develop deep learning- based models to solve two specific tasks, i.e., information cascades modeling and rumor detection. Show less
Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network... Show moreExisting methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at hrips://github.com/LiminLi-xjtu/NetTDP. Show less
Simultaneous inference allows for the exploration of data while deciding on criteria for proclaiming discoveries. It was recently proved that all admissible post hoc inference methods for the true... Show moreSimultaneous inference allows for the exploration of data while deciding on criteria for proclaiming discoveries. It was recently proved that all admissible post hoc inference methods for the true discoveries must employ closed testing. In this paper, we investigate efficient closed testing with local tests of a special form: thresholding a function of sums of test scores for the individual hypotheses. Under this special design, we propose a new statistic that quantifies the cost of multiplicity adjustments, and we develop fast (mostly linear-time) algorithms for post hoc inference. Paired with recent advances in global null tests based on generalized means, our work instantiates a series of simultaneous inference methods that can handle many dependence structures and signal compositions. We provide guidance on the method choices via theoretical investigation of the conservativeness and sensitivity for different local tests, as well as simulations that find analogous behavior for local tests and full closed testing. Show less
Jong, T.A. de; Chen, X.; Jobst, J.; Krasovskii, E.E.; Tromp, R.M.; Molen, S.J. van der 2022
Stacking domain boundaries occur in Van der Waals heterostacks whenever there is a twist angle or lattice mismatch between subsequent layers. Not only can these domain boundaries host topological... Show moreStacking domain boundaries occur in Van der Waals heterostacks whenever there is a twist angle or lattice mismatch between subsequent layers. Not only can these domain boundaries host topological edge states, imaging them has been instrumental to determine local variations in twisted bilayer graphene. Here, we analyse the mechanisms causing stacking domain boundary contrast in Bright Field Low-Energy Electron Microscopy (BF-LEEM) for both graphene on SiC, where domain boundaries are caused by strain and for twisted few layer graphene. We show that when domain boundaries are between the top two graphene layers, BF-LEEM contrast is observed due to amplitude contrast and corresponds well to calculations of the contrast based purely on the local stacking in the domain boundary. Conversely, for deeper-lying domain boundaries, amplitude contrast only provides a weak distinction between the inequivalent stackings in the domains themselves. However, for small domains phase contrast, where electrons from different parts of the unit cell interfere causes a very strong contrast. We derive a general rule-of-thumb of expected BF-LEEM contrast for domain boundaries in Van der Waals materials. Show less
Researchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely... Show moreResearchers, enterprises, and governments have made great efforts to detect misinformation promptly and accurately. Traditional solutions either examine complicated hand-crafted features or rely heavily on the constructed credibility networks to extract useful indicators for discerning false information. However, such approaches require insightful domain expert knowledge and intensive feature engineering that are often non-generalizable. Recent advances in deep learning techniques have spurred learning high-level representations from textual and image content and discovering diffusion patterns with various neural networks. Despite the progress made by these methods, they still face the problem of overdependence on the content features and fail to discriminate against the influence of each user involved in the process of rumor spreading. Different user-aspect information plays different roles in various stages of rumor diffusion, effectively extract features from each aspect, and aggregate the learned features into a unique representation, which has not been well investigated. To address these limitations, we propose a novel model, UMLARD (User-aspect Multi-view Learning with Attention for Rumor Detection), to effectively learn the representation of different views of the users who engaged in spreading the tweet, and fuse the learned features through the distinguishable fusion mechanism. Finally, we concatenate the learned user-aspect features with content features to form a unique representation and feed it into a fully connected layer to predict the label of rumors. Our experiments conducted on real-world datasets demonstrate that UMLARD significantly improves the rumor detection performance compared to state-of-the-art baselines. It also allows explainability of the model behavior and the predicted results. Show less
Capelôa, L.; Yazdi, M.; Zhang, H.; Chen, X.; Nie, Y.; Wagner, E.; ... ; Barz, M. 2021
ABC-type triblock copolymers are a rising platform especially for oligonucleotide delivery as they offer an additional functionality besides the anyhow needed functions of shielding and... Show moreABC-type triblock copolymers are a rising platform especially for oligonucleotide delivery as they offer an additional functionality besides the anyhow needed functions of shielding and complexation. The authors present a polypept(o)ide-based triblock copolymer synthesized by amine-initiated ring-opening polymerization (ROP) of N-carboxyanhydrides (NCAs), comprising a shielding block A of polysarcosine (pSar), a poly(S-ethylsulfonyl-l-cystein) (pCys(SO2 Et)) block B for bioreversible and chemo-selective cross-linking and a poly(l-lysine) (pLys) block C for complexation to construct polyion complex (PIC) micelles as vehicle for small interfering RNA (siRNA) delivery. The self-assembly behavior of ABC-type triblocks is investigated to derive correlations between block lengths of the polymer and PIC micelle structure, showing an enormous effect of the β-sheet forming pCys(SO2 Et) block. Moreover, the block enables the introduction of disulfide cross-links by reaction with multifunctional thiols to increase stability against dilution. The right content of the additional block leads to well-defined cross-linked 50-60 nm PIC micelles purified from production impurities and determinable siRNA loading. These PIC micelles can deliver functional siRNA into Neuro2A and KB cells evaluated by cellular uptake and specific gene knockdown assays. Show less
Information cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted... Show moreInformation cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted features, requiring extensive domain knowledge and hard to generalize to new domains. Recently, inspired by the success of deep learning in computer vision and natural language processing, researchers have developed neural network-based approaches for tackling this problem. However, existing deep learning-based methods either focused on modeling the temporal characteristics of cascades but ignored the structural information or failed to take the order-scale and position-scale into consideration in modeling structures of information propagation. This paper proposed a novel graph neural network-based model, called MUCas, to learn the latent representations of cascade graphs from a multi-scale perspective, which can make full use of the direction-scale, high-order-scale, position-scale, and dynamic-scale of cascades via a newly designed MUlti-scale Graph Capsule Network (MUG-Caps) and the influence-attention mechanism. Extensive experiments conducted on two real-world data sets demonstrate that our MUCas significantly outperforms the state-of-the-art approaches. Show less
Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium... Show moreAims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targets) consortium aims to identify the genomic and molecular basis of heart failure.Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome-wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow-up following heart failure diagnosis ranged from 2 to 116 months. Forty-nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34-90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of >1.10 for common variants (allele frequency > 0.05) and >1.20 for low-frequency variants (allele frequency 0.01-0.05) at P < 5 x 10(-8) under an additive genetic model.Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction. Show less
Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and... Show moreResearchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering. They require lots of human actions and are difficult to generalize. Deep learning solutions come to help. However, they usually fail to capture the underlying structure of the rumor propagation and the influence of all participants involved in the spreading chain. In this study, we propose a novel participant level rumor detection framework. It explicitly models and integrates various fine-grained user representations (i.e., user influence, susceptibility, and temporal information) of all participants from the propagation threads via deep representation learning. Experiments conducted on real world datasets demonstrate a significant accuracy improvement of our approach. Theoretically, we contribute to the effective usage of data science and analytics for social information diffusion design, particularly rumor detection. Practically, our results can be used to improve the quality of rumor detection services for social platforms. Show less
Chen, X.; Jin, R.; Jiang, Q.; Bi, Q.; He, T.; Song, X.; ... ; Nie, Y. 2021
The tumor hypoxic microenvironment not only induces genetic and epigenetic changes in tumor cells, immature vessels formation for oxygen demand, but also compromises the efficiency of therapeutic... Show moreThe tumor hypoxic microenvironment not only induces genetic and epigenetic changes in tumor cells, immature vessels formation for oxygen demand, but also compromises the efficiency of therapeutic interventions. On the other hand, conventional therapeutic approaches which kill tumor cells or destroy tumor blood vessels to block nutrition and oxygen supply usually facilitate even harsher microenvironment. Thus, simultaneously relieving the strained response of tumor cells and blood vessels represents a promising strategy to reverse the adverse tumor hypoxic microenvironment. In the present study, an integrated amphiphilic system (RSCD) is designed based on Angiotensin II receptor blocker candesartan for siRNA delivery against the hypoxia-inducible factor-1 alpha (HIF-1α), aiming at both vascular and cellular "relaxation" to reconstruct a tumor normoxic microenvironment. Both in vitro and in vivo studies have confirmed that the hypoxia-inducible HIF-1α expression is down-regulated by 70% and vascular growth is inhibited by 60%. The "relaxation" therapy enables neovascularization with more complete and organized structures to obviously increase the oxygen level inside tumor, which results in a 50% growth inhibition. Moreover, reconstruction of tumor microenvironment enhances tumor-targeted drug delivery, and significantly improves the chemotherapeutic and photodynamic anticancer treatments. Show less
Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual... Show moreResearchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion-based rumor detection model, called Macroscopic and Microscopic-aware Rumor Detection, to explore the full-scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user-time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real-world data sets demonstrate that our method achieves significant accuracy improvements over the state-of-the-art baseline models on rumor detection. Show less
This thesis consists of five chapters on how to construct prediction sets for different types of data and models in a parametric or nonparametric Bayesian paradigm. The motivation of the models... Show moreThis thesis consists of five chapters on how to construct prediction sets for different types of data and models in a parametric or nonparametric Bayesian paradigm. The motivation of the models comes from applications in pharmaceutical manufacturing and quality control. Show less
